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Statistics Colloquium
March 17 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower
Chenyang Shi
Department of Statistics
Western Michigan University
Abstract: Foster care youth is a medically vulnerable population. Poor dental health and irregular well-child visits may cause serious health-related issues, such as mental disorder, nutrition imbalance, tooth damage, etc. Michigan requires all youth in foster care to receive annual dental and well-child visits. Usually, the study of foster care well-child and dental visits include two parts: time between two consecutive visits (gap time) and number of visits. For this study, an ordinal logit model that has the flexibility to analyze the well-child/dental gap times and number of visits was developed. The longitudinal data (2009-2012) on Michigan foster care youth from 10 years old to 19 years old, with county of residence information was analyzed. The bivariate outcome variables are time between two consecutive well-child and two consecutive dental visits. Explanatory variables include gender, age, race, number of living arrangements, type of living arrangement, and the population in each county. The numbers of visits for each youth were characterized by Poisson distributions. For analyzing gap times, since our data has many abnormal gap times (i.e., well-child visit is an annual visit, so the well-child gap time should be around 1 year, but we have many gap times which are less than 6 month or greater than 2 years), the gap times were categorized.
We divided gap times into three types based on the length of the time: 1). Soon gap time, if the gap time is less than or equal to 10 months (4 months) in well-child (dental) analysis; 2). Appropriate gap times, if the gap time is greater than 10 months (4 months) and less than or equal to 14 months (8 months) in well-child (dental) analysis; 3). Late gap time, if the gap time is greater than 14 months (8 months) in well-child (dental) analysis. After this transformation, the gap times were transformed into categorical data, so that an ordinal logit model could be fitted in terms of the explanatory variables and spatial frailties. In addition, a data-augmentation Markov Chain Monte Carlo (MCMC) algorithm was developed for model fitting, and deviance information criterion (DIC) were used to do model selection.
Bio:
Chenyang Shi has recently successfully defended his doctoral dissertation in statistics.
This is a public presentation of his dissertation.
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All statistics students are expected to attend.